Apply ruff flake8-comprehensions (#21694)

tests/deepspeed/test_model_zoo.py

@@ -166,8 +166,8 @@ def make_task_cmds():
     # but need a tiny model for each
     #
     # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
-    tasks2models = dict(
-        trans=[
+    tasks2models = {
+        "trans": [
             "bart",
             "fsmt",
             "m2m_100",
@@ -177,10 +177,10 @@ def make_task_cmds():
             "t5_v1",
             # "mt5", missing model files
         ],
-        sum=[
+        "sum": [
             "pegasus",
         ],
-        clm=[
+        "clm": [
             "big_bird",
             "bigbird_pegasus",
             "blenderbot",
@@ -192,7 +192,7 @@ def make_task_cmds():
             "prophetnet",
             # "camembert", missing model files
         ],
-        mlm=[
+        "mlm": [
             "albert",
             "deberta",
             "deberta-v2",
@@ -203,7 +203,7 @@ def make_task_cmds():
             "layoutlm",
             # "reformer", # multiple issues with either mlm/qa/clas
         ],
-        qa=[
+        "qa": [
             "led",
             "longformer",
             "mobilebert",
@@ -213,7 +213,7 @@ def make_task_cmds():
             # "convbert", # missing tokenizer files
             # "layoutlmv2", missing model files
         ],
-        clas=[
+        "clas": [
             "bert",
             "xlnet",
             # "hubert", # missing tokenizer files
@@ -223,54 +223,54 @@ def make_task_cmds():
             # "openai-gpt", missing model files
             # "tapas", multiple issues
         ],
-        img_clas=[
+        "img_clas": [
             "vit",
         ],
-    )
+    }
 
     scripts_dir = f"{ROOT_DIRECTORY}/examples/pytorch"
 
-    tasks = dict(
-        trans=f"""
+    tasks = {
+        "trans": f"""
         {scripts_dir}/translation/run_translation.py
         --train_file {data_dir_wmt}/train.json
         --source_lang en
         --target_lang ro
         """,
-        sum=f"""
+        "sum": f"""
         {scripts_dir}/summarization/run_summarization.py
         --train_file {data_dir_xsum}/sample.json
         --max_source_length 12
         --max_target_length 12
         --lang en
         """,
-        clm=f"""
+        "clm": f"""
         {scripts_dir}/language-modeling/run_clm.py
         --train_file {FIXTURE_DIRECTORY}/sample_text.txt
         --block_size 8
         """,
-        mlm=f"""
+        "mlm": f"""
         {scripts_dir}/language-modeling/run_mlm.py
         --train_file {FIXTURE_DIRECTORY}/sample_text.txt
         """,
-        qa=f"""
+        "qa": f"""
         {scripts_dir}/question-answering/run_qa.py
         --train_file {data_dir_samples}/SQUAD/sample.json
         """,
-        clas=f"""
+        "clas": f"""
         {scripts_dir}/text-classification/run_glue.py
         --train_file {data_dir_samples}/MRPC/train.csv
         --max_seq_length 12
         --task_name MRPC
         """,
-        img_clas=f"""
+        "img_clas": f"""
         {scripts_dir}/image-classification/run_image_classification.py
             --dataset_name hf-internal-testing/cats_vs_dogs_sample
             --remove_unused_columns False
             --max_steps 10
             --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
         """,
-    )
+    }
 
     launcher = get_launcher(distributed=True)
 

tests/extended/test_trainer_ext.py

@@ -155,21 +155,21 @@ class TestTrainerExt(TestCasePlus):
     @require_torch_multi_gpu
     def test_trainer_log_level_replica(self, experiment_id):
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
-        experiments = dict(
+        experiments = {
             # test with the default log_level - should be info and thus log info once
-            base=dict(extra_args_str="", n_matches=1),
+            "base": {"extra_args_str": "", "n_matches": 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
-            low=dict(extra_args_str="--log_level debug --log_level_replica debug", n_matches=2),
+            "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
-            high=dict(extra_args_str="--log_level error --log_level_replica debug", n_matches=1),
+            "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
-            mixed=dict(extra_args_str="--log_level error --log_level_replica error", n_matches=0),
-        )
+            "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
+        }
 
         data = experiments[experiment_id]
-        kwargs = dict(distributed=True, predict_with_generate=False, do_eval=False, do_predict=False)
+        kwargs = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
         log_info_string = "Running training"
         with CaptureStderr() as cl:
             self.run_seq2seq_quick(**kwargs, extra_args_str=data["extra_args_str"])

tests/generation/test_utils.py

@@ -1480,7 +1480,7 @@ class GenerationTesterMixin:
 
             signature = inspect.signature(model.forward)
             # We want to test only models where encoder/decoder head masking is implemented
-            if not set(head_masking.keys()) < set([*signature.parameters.keys()]):
+            if not set(head_masking.keys()) < {*signature.parameters.keys()}:
                 continue
 
             for attn_name, (name, mask) in zip(attention_names, head_masking.items()):

tests/models/bart/test_modeling_bart.py

@@ -939,7 +939,7 @@ class BartModelIntegrationTests(unittest.TestCase):
 
     def test_xsum_config_generation_params(self):
         config = BartConfig.from_pretrained("facebook/bart-large-xsum")
-        expected_params = dict(num_beams=6, do_sample=False, early_stopping=True, length_penalty=1.0)
+        expected_params = {"num_beams": 6, "do_sample": False, "early_stopping": True, "length_penalty": 1.0}
         config_params = {k: getattr(config, k, "MISSING") for k, v in expected_params.items()}
         self.assertDictEqual(expected_params, config_params)
 

tests/models/blenderbot/test_modeling_blenderbot.py

@@ -299,8 +299,8 @@ class Blenderbot3BIntegrationTests(unittest.TestCase):
 
     @slow
     def test_generation_from_short_input_same_as_parlai_3B(self):
-        FASTER_GEN_KWARGS = dict(num_beams=1, early_stopping=True, min_length=15, max_length=25)
-        TOK_DECODE_KW = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        FASTER_GEN_KWARGS = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
+        TOK_DECODE_KW = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}
 
         torch.cuda.empty_cache()
         model = BlenderbotForConditionalGeneration.from_pretrained(self.ckpt).half().to(torch_device)

tests/models/blenderbot/test_modeling_flax_blenderbot.py

@@ -402,8 +402,8 @@ class FlaxBlenderbotModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGener
     @unittest.skipUnless(jax_device != "cpu", "3B test too slow on CPU.")
     @slow
     def test_generation_from_short_input_same_as_parlai_3B(self):
-        FASTER_GEN_KWARGS = dict(num_beams=1, early_stopping=True, min_length=15, max_length=25)
-        TOK_DECODE_KW = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        FASTER_GEN_KWARGS = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
+        TOK_DECODE_KW = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}
 
         model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B", from_pt=True)
         tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")

tests/models/bloom/test_tokenization_bloom.py

@@ -124,7 +124,7 @@ class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         input_text = list(sample_data.values())
 
         output_tokens = list(map(tokenizer.encode, input_text))
-        predicted_text = list(map(lambda x: tokenizer.decode(x, clean_up_tokenization_spaces=False), output_tokens))
+        predicted_text = [tokenizer.decode(x, clean_up_tokenization_spaces=False) for x in output_tokens]
         self.assertListEqual(predicted_text, input_text)
 
     def test_pretrained_model_lists(self):

tests/models/clip/test_modeling_tf_clip.py

@@ -551,7 +551,7 @@ class TFCLIPModelTest(TFModelTesterMixin, unittest.TestCase):
         if self.__class__.__name__ == "TFCLIPModelTest":
             inputs_dict.pop("return_loss", None)
 
-        tf_main_layer_classes = set(
+        tf_main_layer_classes = {
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__),)
@@ -563,7 +563,7 @@ class TFCLIPModelTest(TFModelTesterMixin, unittest.TestCase):
             if isinstance(module_member, type)
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(module_member, "_keras_serializable", False)
-        )
+        }
         for main_layer_class in tf_main_layer_classes:
             # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
             if "T5" in main_layer_class.__name__:

tests/models/data2vec/test_modeling_tf_data2vec_vision.py

@@ -398,7 +398,7 @@ class TFData2VecVisionModelTest(TFModelTesterMixin, unittest.TestCase):
                     # The number of elements in the loss should be the same as the number of elements in the label
                     _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit()
                     added_label = prepared_for_class[
-                        sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
+                        sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
                     ]
                     loss_size = tf.size(added_label)
 

tests/models/groupvit/test_modeling_tf_groupvit.py

@@ -628,7 +628,7 @@ class TFGroupViTModelTest(TFModelTesterMixin, unittest.TestCase):
         if self.__class__.__name__ == "TFGroupViTModelTest":
             inputs_dict.pop("return_loss", None)
 
-        tf_main_layer_classes = set(
+        tf_main_layer_classes = {
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__),)
@@ -640,7 +640,7 @@ class TFGroupViTModelTest(TFModelTesterMixin, unittest.TestCase):
             if isinstance(module_member, type)
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(module_member, "_keras_serializable", False)
-        )
+        }
         for main_layer_class in tf_main_layer_classes:
             # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
             if "T5" in main_layer_class.__name__:

tests/models/jukebox/test_modeling_jukebox.py

@@ -30,10 +30,10 @@ if is_torch_available():
 class Jukebox1bModelTester(unittest.TestCase):
     all_model_classes = (JukeboxModel,) if is_torch_available() else ()
     model_id = "openai/jukebox-1b-lyrics"
-    metas = dict(
-        artist="Zac Brown Band",
-        genres="Country",
-        lyrics="""I met a traveller from an antique land,
+    metas = {
+        "artist": "Zac Brown Band",
+        "genres": "Country",
+        "lyrics": """I met a traveller from an antique land,
     Who said "Two vast and trunkless legs of stone
     Stand in the desert. . . . Near them, on the sand,
     Half sunk a shattered visage lies, whose frown,
@@ -48,7 +48,7 @@ class Jukebox1bModelTester(unittest.TestCase):
     Of that colossal Wreck, boundless and bare
     The lone and level sands stretch far away
     """,
-    )
+    }
     # fmt: off
     EXPECTED_OUTPUT_2 = [
         1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
@@ -180,7 +180,7 @@ class Jukebox1bModelTester(unittest.TestCase):
         model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
         set_seed(0)
         waveform = torch.rand((1, 5120, 1))
-        tokens = [i for i in self.prepare_inputs()]
+        tokens = list(self.prepare_inputs())
 
         zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None]
         zs = model._sample(
@@ -220,10 +220,10 @@ class Jukebox1bModelTester(unittest.TestCase):
 class Jukebox5bModelTester(unittest.TestCase):
     all_model_classes = (JukeboxModel,) if is_torch_available() else ()
     model_id = "openai/jukebox-5b-lyrics"
-    metas = dict(
-        artist="Zac Brown Band",
-        genres="Country",
-        lyrics="""I met a traveller from an antique land,
+    metas = {
+        "artist": "Zac Brown Band",
+        "genres": "Country",
+        "lyrics": """I met a traveller from an antique land,
     Who said "Two vast and trunkless legs of stone
     Stand in the desert. . . . Near them, on the sand,
     Half sunk a shattered visage lies, whose frown,
@@ -238,7 +238,7 @@ class Jukebox5bModelTester(unittest.TestCase):
     Of that colossal Wreck, boundless and bare
     The lone and level sands stretch far away
     """,
-    )
+    }
 
     # fmt: off
     EXPECTED_OUTPUT_2 = [

tests/models/jukebox/test_tokenization_jukebox.py

@@ -21,10 +21,10 @@ from transformers.testing_utils import require_torch
 
 class JukeboxTokenizationTest(unittest.TestCase):
     tokenizer_class = JukeboxTokenizer
-    metas = dict(
-        artist="Zac Brown Band",
-        genres="Country",
-        lyrics="""I met a traveller from an antique land,
+    metas = {
+        "artist": "Zac Brown Band",
+        "genres": "Country",
+        "lyrics": """I met a traveller from an antique land,
         Who said "Two vast and trunkless legs of stone
         Stand in the desert. . . . Near them, on the sand,
         Half sunk a shattered visage lies, whose frown,
@@ -39,7 +39,7 @@ class JukeboxTokenizationTest(unittest.TestCase):
         Of that colossal Wreck, boundless and bare
         The lone and level sands stretch far away
         """,
-    )
+    }
 
     @require_torch
     def test_1b_lyrics_tokenizer(self):

tests/models/layoutlmv2/test_processor_layoutlmv2.py

@@ -233,7 +233,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify image
@@ -253,7 +253,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify images
@@ -301,7 +301,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -340,7 +340,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "labels", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -362,7 +362,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "labels", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -403,7 +403,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -422,7 +422,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -456,7 +456,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -472,7 +472,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids

tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py

@@ -320,7 +320,7 @@ class TFLayoutLMv3ModelTest(TFModelTesterMixin, unittest.TestCase):
                 # The number of elements in the loss should be the same as the number of elements in the label
                 prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                 added_label = prepared_for_class[
-                    sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
+                    sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
                 ]
                 expected_loss_size = added_label.shape.as_list()[:1]
 

tests/models/layoutlmv3/test_processor_layoutlmv3.py

@@ -213,7 +213,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify image
@@ -235,7 +235,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify images
@@ -285,7 +285,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -324,7 +324,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -346,7 +346,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -387,7 +387,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -406,7 +406,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -440,7 +440,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -456,7 +456,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids

tests/models/layoutxlm/test_processor_layoutxlm.py

@@ -228,7 +228,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify image
@@ -250,7 +250,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify images
@@ -300,7 +300,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -339,7 +339,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "labels"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -361,7 +361,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids", "labels"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -402,7 +402,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -421,7 +421,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -455,7 +455,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -471,7 +471,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "image", "input_ids"]
-            actual_keys = sorted(list(input_processor.keys()))
+            actual_keys = sorted(input_processor.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids

tests/models/markuplm/test_processor_markuplm.py

@@ -204,7 +204,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -216,7 +216,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -260,7 +260,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -294,7 +294,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
                 "xpath_subs_seq",
                 "xpath_tags_seq",
             ]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -331,7 +331,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
                 "xpath_subs_seq",
                 "xpath_tags_seq",
             ]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -367,7 +367,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -390,7 +390,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -425,7 +425,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids
@@ -444,7 +444,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase):
 
             # verify keys
             expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
-            actual_keys = sorted(list(inputs.keys()))
+            actual_keys = sorted(inputs.keys())
             self.assertListEqual(actual_keys, expected_keys)
 
             # verify input_ids

tests/models/mobilevit/test_modeling_tf_mobilevit.py

@@ -295,7 +295,7 @@ class MobileViTModelTest(TFModelTesterMixin, unittest.TestCase):
                 # The number of elements in the loss should be the same as the number of elements in the label
                 prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                 added_label = prepared_for_class[
-                    sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
+                    sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
                 ]
                 expected_loss_size = added_label.shape.as_list()[:1]
 

tests/models/perceiver/test_modeling_perceiver.py

@@ -166,9 +166,11 @@ class PerceiverModelTester:
             audio = torch.randn(
                 (self.batch_size, self.num_frames * self.audio_samples_per_frame, 1), device=torch_device
             )
-            inputs = dict(
-                image=images, audio=audio, label=torch.zeros((self.batch_size, self.num_labels), device=torch_device)
-            )
+            inputs = {
+                "image": images,
+                "audio": audio,
+                "label": torch.zeros((self.batch_size, self.num_labels), device=torch_device),
+            }
         else:
             raise ValueError(f"Model class {model_class} not supported")
 
@@ -734,7 +736,7 @@ class PerceiverModelTest(ModelTesterMixin, unittest.TestCase):
                 continue
 
             config, inputs, input_mask, _, _ = self.model_tester.prepare_config_and_inputs(model_class=model_class)
-            inputs_dict = dict(inputs=inputs, attention_mask=input_mask)
+            inputs_dict = {"inputs": inputs, "attention_mask": input_mask}
 
             for problem_type in problem_types:
                 with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):

tests/models/roc_bert/test_tokenization_roc_bert.py

@@ -44,8 +44,8 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         super().setUp()
 
         vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
-        word_shape = dict()
-        word_pronunciation = dict()
+        word_shape = {}
+        word_pronunciation = {}
         for i, value in enumerate(vocab_tokens):
             word_shape[value] = i
             word_pronunciation[value] = i